Using explanatory item response models to analyze group differences in science achievement

Applied Measurement in Education

Volumn 21, Issue 2, 2008, Pages 89-118

  Briggs, Derek C ^a  

^a UNIVERSITY OF COLORADO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 41849147187     PISSN: 08957347     EISSN: None     Source Type: Journal    
DOI: 10.1080/08957340801926086     Document Type: Article

References (46)

1. Ackerman, T. (1994). Using multidimensional item response theory to understand what items and tests are measuring. Applied Measurement in Education, 7, 255-278.
2. Ackerman, T., Gierl, M. J., & Walker, C. M. (2003). Using multidimensional IRT to evaluate educational and psychological tests. Educational Measurement: Issues and Practice. Fall 2003, 37-53.
3. Adams, R. (2006). Reliability as a measurement design effect. Paper presented at the 2006 Bi-annual meeting of the Institute for Objective Measurement Workshop. Berkeley, CA.
4. Adams, R., Wilson, M., & Wu, M. (1997). Multilevel item response models: an approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22(1), 46-75.
5. Adams, R. J., Wilson, M., & Wang, W. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement 21(1), 1-23.
6. American Association for the Advancement of Science. (1993). Benchmarks for scientific literacy. New York: Oxford University Press.
7. Bock, R. D., & Aitken, M. (1981). Marginal maximum likelihood estimation of item parameters: An application of the EM algorithm. Psychometrika, 46, 443-459.
8. Breslow, N. E., & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association. 88, 9-25.
9. Briggs, D. C., & Wilson, M. (2003). An introduction to multidimensional measurement using Rasch model s. Journal of Applied Measurement, 4(1), 87-100.
10. Camara, W. J., and Schmidt, A. E. (1999). Group differences in standardized testing and social stratification. New York: The College Board, 1-18.
11. Campbell, J. R., Hombo, C. M., & Mazzeo, J. (2000). NAEP 1999 trends in academic progress: Three decades of student performance. Washington, DC, U.S. Department of Education. Office of Educational Research and Improvement. National Center for Education Statistics.
12. Davidian, M., & Gilitinan, D. M. (1995). Nonlinear models for repeated measurement data. London: Chapman & Hall.
13. De Boeck, P., & Wilson, M., eds. (2004). Explanatory item response models: A generalized linear and nonlinear approach. New York: Springer.
14. Embretson, S. E. & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.
15. Gelman, A., Carlin. J., Stern, H., & Rubin, D. (2004). Bayesian data analysis, 2nd Edition. London: Chapman & Hall/CRC Press.
16. Green, B., Bock, R., Humphreys, L., Linn, R., & Reckase, M. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21, 347-360.
17. Janssen, R., Schepers, J., & Peres, D. (2004). Models with item and item group predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 189-212). New York: Springer.
18. Jencks. C., & Phillips, M. (1998). The black-white test score gap. Washington. DC: Brookings Institution Press.
19. Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational Measurement, 38(1), 79-93.
20. Lee, J. (2002). Racial and ethnic achievement gap trends: Reversing the progress toward equity? Educational Researcher, 31(1), 3-12.
21. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
22. McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models. New York: Wiley.
23. Meulders, M., & Xie, Y. (2004). Person-by-item predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 214-240). New York: Springer.
24. Mislevy, R. J., Beaton, A. E., Kaplan., B., & Sheehan, K. M. (1992). Estimating population characteristics from sparse matrix samples of item responses. Journal of Educational Measurement, 29, 133-161.
25. Molenberghs, G., & Verbeke, G. (2004). An introduction to generalized (non)linear mixed models. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 111-153). New York: Springer.
26. Monfils, L., Dawber, T., Han, N., & Henderson-Montrero, D. (2006). Supporting reform efforts through diagnostic subscore reports: Implications for schools. Paper presented at the 2006 annual meeting of the National Council for Measurement in Education. San Francisco, CA.
27. Nandakumar, R., & Stout, W. (1993). Refinements of Stout's procedure for assessing latent trait unidimensionality. Journal of Educational Statistics, 18, 41-68.
28. National Research Council. (1996). National science education standards. Washington, DC: National Academy Press. 262.
29. Reckase, M. D., & McKinley, R. L. (1991). The discriminating power of items that measure more than one dimension. Applied Psychological Measurement, 15, 361-373.
30. Rijmen, F., & Briggs, D. C. (2004). Multiple person dimensions and latent item predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 247-265). New York: Springer.
31. Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185-205.
32. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. London: Chapman & Hall/CRC.
33. Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2004). WinBUGS 1.4. http://www.mrcbsu.cam.ac.uk/bugs
34. Stout, W. (1987). A nonparametric approach for assessing latent trait unidimensionality. Psychometrika. 52, 589-617.
35. Stout, W., Froelich, A. G., & Gao, F. (2001). Using resampling to produce an improved DIMTEST procedure. In A. Boomsma, M. A. J. van Duijn, & T. A. B. Snijders (Eds.), Essays on item response theory (pp. 357-376). New York: Springer.
36. Thissen, D., Chen. W.-H., & Bock, R. D. (2002). MULTILOG 7. Lincolnwood. IL: Scientific Software.
37. Thissen, D. & Orlando, M. (2001). In D. Thissen & H. Wainer (Eds.), Test scoring (pp. 73-140). Mahwah, NJ: Lawrence Erlbaum Associates.
38. Tuerlinckx, F., Rijmen, F., Molenberghs, G.. Verbeke, G., Briggs, D., Van den Noorgate, W., Meulders, M, & De Boeck, P. (2004). Estimation and software. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach, (pp. 343-373). New York: Springer.
39. Van den Noortgate, W., and Paek, I. (2004). Person regression models. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 167-185). New York: Springer.
40. Verbeke, G. & Molenberghs, G. (1997). Linear mixed models in practice: A SAS-oriented approach. New York: Springer.
41. Verhlest, N., & Verstralen, H. (2001). An IRT model for multiple raters. In A. Boomsma, M van Duijn. & T. Snijders (Eds.), Essays on item response theory (pp. 88-108). New York: Springer-Verlag.
42. Wainer, H., Vevea. J. L., Camacho, F., Reeve, B. B. III, Rosa, K., Nelson, L., Swygert, K. A., & Thissen, D. (2001). Augmented Scores-"Borrowing strength" to compute scores based on small numbers of items. In D. Thissen & H. Wainer (Eds.), Test scoring (pp. 343-387). Mahwah, NJ: Lawrence Erlbaum Associates.
43. Wang, W.-C,, Wilson, M., & Adams, R. (1997). Rasch models for multidimensionality between items and within items. In G. Englehard & Mark Wilson (Eds.), Objective measurement (Vol. 4, pp. 139-155). Norwood, NY: Ablex Publishing.
44. WestEd (2006). Partnership for the Assessment of Standards-based Science: FAQs and Sample Assessment Items. San Francisco, CA. http://www.wested.org/cs/ we/view/rs/612 Retrieved from website June 27, 2006.
45. Wu, M. L., Adams, Raymond J., & Wilson, Mark R. (1998). ACER Conquest. Melbourne, Australia, Australian Council for Educational Research.
46. Zimowski, M., Muraki. E., Mislevy, R. J., & Bock. R. D. (1995). BILOG-MG: Multiple-group item analysis and test scoring. Chicago. IL: Scientific Software.